In industries such as banking, finance, and insurance, proper classification of data is critical to comply with a number of regulations that can vary based on geographic location, and may be further complicated by the assignment of different classification codes for similar data depending upon geographic location. For example, in the insurance industry, workers compensation classification codes can be three or four digits as assigned by the National Council on Compensation Insurance (NCCI) or state rating bureaus. In the context of workers compensation insurance, over 700 unique classification codes exist, which serve as the basis for pricing and underwriting of workers compensation insurance policies. The classification codes help to differentiate between the various job duties or “scope of work performed” by employees.
Classification codes that are regulated by industry and geographic location are subject to change as time passes. Regulating bodies need not coordinate updates relative to each other, leading to any number of changes at any time throughout the year. The highly regulated yet dynamic nature of classification codes can make it challenging for professionals to stay current on exact numeric codes and associated definitions. This can result in a time consuming process of frequently researching, locating, and identifying proper classification codes using a number of industry sites and/or publications. As one example, when creating a new insurance policy, an insurance professional can seek out and manually input the proper classification codes when entering various attributes that describe risks and/or operations of the policy applicant. For policies that span many job types and geographic locations, the risk of error or misclassification increases where outdated classification codes can be used and free-form manual data entry is performed.
When manual searches of various data sources are performed to determine classification codes, knowledge of contents and formatting of the data sources may be required. For example, when searching for a classification code by its associated description, a keyword search may be performed to identify the associated description. However, the keyword search may be ineffective if the user does not know exactly how the description is worded, and thus may fail to accurately locate the correct classification code. Different descriptions can be assigned to the same classification code for different geographic locations. Moreover, a single classification code can be assigned to multiple descriptions for the same geographic location. Accordingly, several attempts may be needed to craft a search string that aligns with varying description definitions and formats in the data sources. This variability can result in additional time needed to populate forms that use classification codes, as searches may be reformatted and repeated across various data sources to capture one or more desired classification codes.